library(dplyr)
library(magrittr)
setClass('ampseq', slots = c(
gt = "ANY",
metadata = "ANY",
markers = "ANY",
loci_performance = "ANY",
pop_summary = "ANY",
controls = "ANY",
discarded_loci = "ANY",
discarded_samples = "ANY",
plots = "ANY"
))
load(params$RData_image)
The haplotype of each sample is stored in the table
aacigar_table within the object
drug_resistant_haplotypes_plot.
plot_relatedness_distribution_between$plot
Figure 1: IBD distribution between sites
plot_frac_highly_related_between$plot
Figure 2: Frequency of highly related among sites
if(!is.na(Variable2)){plot_frac_highly_related_overtime_between$plot_IBD_correlation_matrix}
Figure 2: Frequency of highly related between sites over time
if(!is.na(Variable2)){plot_frac_highly_related_overtime_between$plot_frac_highly_related}
Figure 2: Frequency of highly related between sites over time
IBD_PCA
Figure 3: IBD PCA
plot_network(pairwise_relatedness,
threshold = ibd_thres,
metadata = ampseq_object@metadata,
sample_id = 'Sample_id',
group_by = Variable1,
levels = levels(as.factor(ampseq_object@metadata[[Variable1]])),
colors = brewer.pal(n = nlevels(as.factor(ampseq_object@metadata[[Variable1]])), name = 'Accent')
)
Figure 4: IBD network
## $network_object
## IGRAPH f101f9d UN-- 844 24772 --
## + attr: name (v/c)
## + edges from f101f9d (vertex names):
## [1] ID00033--ID01021 ID00033--ID01031 ID00033--ID01032 ID00033--ID00052
## [5] ID00033--ID00058 ID00033--ID00301 ID00033--ID00350 ID00033--ID01043
## [9] ID00033--ID01044 ID00033--ID01045 ID00033--ID01047 ID00033--ID01051
## [13] ID00033--ID01058 ID00033--ID01075 ID00033--ID01080 ID00033--ID01081
## [17] ID00033--ID01113 ID00033--ID01124 ID00033--ID01125 ID00033--ID01165
## [21] ID00033--ID01168 ID00033--ID01173 ID00033--ID01126 ID00033--ID01127
## [25] ID00033--ID01134 ID00033--ID01142 ID00033--ID01149 ID00033--ID01150
## [29] ID00034--ID00039 ID00034--ID00065 ID00034--ID00089 ID00034--ID00092
## + ... omitted several edges
##
## $plot_network
## NULL